PROJECT SUMMARY Capturing proteomic signatures of a disease in body fluids like plasma and cerebrospinal fluid (CSF) can provide valuable insights into the underlying mechanisms as well as potential diagnostic, prognostic, and disease monitoring markers. Large-scale sample analysis provides a unique opportunity to account for biological, environmental and genetic impacts on the disease signatures, and can provide new avenues for personalized biomarkers. Relative to plasma, CSF directly interacts with the brain and provides a more direct window into the pathophysiology of neurodegenerative disease like Parkinson?s disease (PD). The discovery of clinically useful biomarkers from body fluids require three key factors: setting and adhering to a high standard of precision and accuracy in discovery proteomic workflows; Cutting-edge analytical and bioinformatics platforms; and the ability to scale workflows for sufficient sample cohorts to capture biological diversity inherent in proteomic signatures. Our group already has a developed a number of proteomics pipelines specifically designed and tested to provide reproducible and scalable workflows. These require optimization and tailoring for CSF to maximize proteome coverage in PD. Beyond the quantitative accounting of proteins, a rich disease-specific biochemical mosaic exists through specific protein isoforms and modifications. Excitingly, we have developed tools to evaluate two plasma-stable posttranslational modifications (PTMs), acetylation and methylation, derived from the same datasets generated to assess protein concentration changes. Disease induced isoforms and posttranslationally modified protein moieties provide additional and complementary degrees of specificity to protein concentration differences alone. Their analyses are particularly informative in providing mechanistic insights of diseases. In Aim 1 we will establish a workflow for the proteomic analysis of CSF and plasma. We will identify and address pre-analytical variables using established plasma proteomic pipelines and two pipelines that will be tailored for CSF with a view to maximizing analytical performance. This will be accomplished by determining the LLOD, LLOQ, linearity, and the inter- and intra-day coefficients of variance for each peptide and protein observed by these pipelines. In Aim 2 we will apply discovery workflows to generate proteomic candidates for pathophysiological disease biomarkers. We will identify PD-related pathological pathways and signatures by specifically including isoform and disease- associated/induced methylation and acetylation PTMs, in plasma and CSF. We will carry out rigorous and deep proteome phenotyping and provide complete data transparency and availability through the AMP PD portal. In summary, this study will enable us to tailor our expertise in developing robust pipelines for proteomic biomarker discovery to the analysis of protein expression changes and proteoform generation in plasma and CSF, and to use these rich datasets to decipher new diagnostic prognostic, and tracking signatures in Parkinson?s disease.